RecDNN: deep neural network for image reconstruction from limited view projection data

Machine learning assists in increasing the time resolution of X-ray computed tomography applied to mineral precipitation in porous media

Many subsurface engineering technologies or natural processes cause porous medium properties, such as porosity or permeability, to evolve in time. Studying and understanding such processes on the pore scale is strongly aided by visualizing the details of geometric and morphological changes in the pores. For realistic 3D porous media, X-Ray Computed Tomography (XRCT) is the method of choice for visualization. However, the necessary high spatial resolution requires either access to limited high-energy synchrotron facilities or data acquisition times which are considerably longer (e.g. hours) than the time scales of the processes causing the pore geometry change (e.g. minutes). Thus, so far, conventional benchtop XRCT technologies are often too slow to allow for studying dynamic processes. Interrupting experiments for performing XRCT scans is also in many instances no viable approach. We propose a novel workflow for investigating dynamic precipitation processes in porous media systems in 3D using a conventional XRCT technology. Our workflow is based on limiting the data acquisition time by reducing the number of projections and enhancing the lower-quality reconstructed images using machine-learning algorithms trained on images reconstructed from high-quality initial- and final-stage scans. We apply the proposed workflow to induced carbonate precipitation within a porous-media sample of sintered glass-beads. So we were able to increase the temporal resolution sufficiently to study the temporal evolution of the precipitate accumulation using an available benchtop XRCT device.

Exact reconstruction condition for angle-limited computed tomography of chemiluminescence

Computed tomography of chemiluminescence (CTC) is an effective technique for three-dimensional (3D) combustion diagnostics. It reconstructs the 3D concentrations of intermediate species or 3D images of flame topology by multiple chemiluminescence projections captured from different perspectives. In the previous studies of CTC systems, it was assumed that projections from arbitrary perspectives are available. However, for some practical applications, the range of view angles and the number of projections might be restricted due to the optical access limitation, greatly affecting the reconstruction quality. In this paper, the exact reconstruction condition for angle-limited computed tomography of chemiluminescence was studied based on Mojette transform theories, and it was demonstrated by numerical simulations and experiments. The studies indicate that the object tested within limited angles can be well reconstructed when the number of grids, the number of projections, and the sampling rate of projections satisfy the exact reconstruction condition. By increasing the sampling rate of projections, high-quality tomographic reconstruction can be achieved by a few projections in a small angle range. Although this technique is discussed under combustion diagnostics, it can also be used and adapted for other tomography methods.

LoDoPaB-CT, a benchmark dataset for low-dose computed tomography reconstruction

Deep learning approaches for tomographic image reconstruction have become very effective and have been demonstrated to be competitive in the field. Comparing these approaches is a challenging task as they rely to a great extent on the data and setup used for training. With the Low-Dose Parallel Beam (LoDoPaB)-CT dataset, we provide a comprehensive, open-access database of computed tomography images and simulated low photon count measurements. It is suitable for training and comparing deep learning methods as well as classical reconstruction approaches. The dataset contains over 40000 scan slices from around 800 patients selected from the LIDC/IDRI database. The data selection and simulation setup are described in detail, and the generating script is publicly accessible. In addition, we provide a Python library for simplified access to the dataset and an online reconstruction challenge. Furthermore, the dataset can also be used for transfer learning as well as sparse and limited-angle reconstruction scenarios.

Measurement(s)	Low Dose Computed Tomography of the Chest • feature extraction objective
Technology Type(s)	digital curation • image processing technique
Sample Characteristic - Organism	Homo sapiens

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.13526360